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<a href="_energy_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       -</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * \author      -</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> */</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#ifndef SHARK_UNSUPERVISED_RBm_ENERGY_H</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#define SHARK_UNSUPERVISED_RBm_ENERGY_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#include &lt;<a class="code" href="_base_8h.html">shark/LinAlg/Base.h</a>&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_batch_interface_8h.html">shark/Data/BatchInterface.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment"></span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">/// \brief The Energy function determining the Gibbs distribution of an RBM.</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">///</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">///General Energy function which uses the information given by the neurons to automatize</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">///the calculation of the value of the energy for certain states, the derivative of the energy</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">///and the factorization of the probability.</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// Following (but slightly simplifying from the formulas given by) </span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// Welling at al.  a general form of an RBM&#39;s Energy function is given by </span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// \f$ E(\vec v,\vec h)=  f_h(\vec h) + f_v(\vec v) +  \sum_{k,l} \phi_{hk}(\vec h) W_{k,l} \phi_{vl}(\vec v) \f$</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// We call \f$ f_h(\vec h) \f$ and  \f$ f_v(\vec v) \f$ the term of the Energy (energy term) </span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// associated to the hidden or the visible neurons respectively.</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// \f$  \sum_{k,l} \phi_{hk}(\vec h) W_{k,l} \phi_{vl}(\vec v) \f$ is called the interaction term.</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// In the standard case of an binary RBM we have \f$ f_h(\vec h) = \vec h  \vec c \f$</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// and \f$ f_v(\vec v) = \vec v \vec b \f$, where \f$ \vec c \f$ and \f$ \vec b \f$</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// are the vectors of the bias parameters for the hidden and the visible neurons respectively.</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// Furthermore, the interaction term simplifies to \f$ \vec h W \vec v \f$, so we have just</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// one singe &#39;phi-function&#39; for each layer that is the identity function. </span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment"></span>    </div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> RBM&gt;</div>
<div class="foldopen" id="foldopen00057" data-start="{" data-end="};">
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html">   57</a></span><span class="keyword">struct </span><a class="code hl_struct" href="structshark_1_1_energy.html" title="The Energy function determining the Gibbs distribution of an RBM.">Energy</a>{</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#aff1c0d419e5e79be2221262c2321a7c8">   58</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a1b20cbe042d3ac817fbf26c562e5b277" title="type of the hidden layer">RBM::HiddenType</a> <a class="code hl_typedef" href="structshark_1_1_energy.html#aff1c0d419e5e79be2221262c2321a7c8">HiddenType</a>; <span class="comment">//&lt; type of the hidden layer</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#ab71e2d1ae4d13995eaccf3b4992f3593">   59</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a5e271a43da1f3c33db74235402d7a84b" title="type of the visible layer">RBM::VisibleType</a> <a class="code hl_typedef" href="structshark_1_1_energy.html#ab71e2d1ae4d13995eaccf3b4992f3593">VisibleType</a>; <span class="comment">//&lt; type of the visible layer</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    </div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    <span class="comment">//typedefs for single element</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a5d806deb535c0978328154e4436bab50">   62</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> HiddenType::SufficientStatistics <a class="code hl_typedef" href="structshark_1_1_energy.html#a5d806deb535c0978328154e4436bab50">HiddenStatistics</a>;</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a27298f912ce91884f0d4b26832f971b6">   63</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> VisibleType::SufficientStatistics <a class="code hl_typedef" href="structshark_1_1_energy.html#a27298f912ce91884f0d4b26832f971b6">VisibleStatistics</a>;</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    </div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    <span class="comment">//batch typedefs</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#adb7c41575f52f5cfa9ac63161fc54c93">   66</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> HiddenType::StatisticsBatch <a class="code hl_typedef" href="structshark_1_1_energy.html#adb7c41575f52f5cfa9ac63161fc54c93">HiddenStatisticsBatch</a>;</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a8b34b676d95d754b53ce625e587b9250">   67</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> VisibleType::StatisticsBatch <a class="code hl_typedef" href="structshark_1_1_energy.html#a8b34b676d95d754b53ce625e587b9250">VisibleStatisticsBatch</a>;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span> </div>
<div class="foldopen" id="foldopen00069" data-start="{" data-end="}">
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a18af216e713128efcd3b7dbfbd38ec5d">   69</a></span>    <a class="code hl_function" href="structshark_1_1_energy.html#a18af216e713128efcd3b7dbfbd38ec5d">Energy</a>(<a class="code hl_class" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a> <span class="keyword">const</span>&amp; rbm)</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    : m_rbm(rbm)</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    , m_hiddenNeurons(rbm.hiddenNeurons())</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    , m_visibleNeurons(rbm.visibleNeurons()){}</div>
</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="comment"></span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">    ///\brief Calculates the Energy given the states of batches of hidden and visible variables .</span></div>
<div class="foldopen" id="foldopen00075" data-start="{" data-end="}">
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#aa07edf12f92820285d1b51495cdafb35">   75</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#aa07edf12f92820285d1b51495cdafb35" title="Calculates the Energy given the states of batches of hidden and visible variables .">energy</a>(RealMatrix <span class="keyword">const</span>&amp; hidden, RealMatrix <span class="keyword">const</span>&amp; visible)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(visible.size1() == hidden.size1());</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = visible.size1();</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>        RealMatrix input(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>,m_hiddenNeurons.size());</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <a class="code hl_function" href="structshark_1_1_energy.html#a751c81c6de87a9d563d36db38edccb92" title="Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion.">inputHidden</a>( input, visible);</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        </div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="structshark_1_1_energy.html#a25ad87baa9a3500ea7b3af9a4effc933" title="Optimization of the calculation of the energy, when the input of the hidden units is already availabl...">energyFromHiddenInput</a>( input, hidden, visible);</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    }</div>
</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    <span class="comment"></span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">    ///\brief Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion.</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    ///@param inputs the batch of vectors the input of the hidden neurons is stored in</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">    ///@param visibleStates the batch of states of the visible neurons@</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">    ///@todo Remove this and replace fully by the rbm method if possible</span></div>
<div class="foldopen" id="foldopen00090" data-start="{" data-end="}">
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a751c81c6de87a9d563d36db38edccb92">   90</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="structshark_1_1_energy.html#a751c81c6de87a9d563d36db38edccb92" title="Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion.">inputHidden</a>(RealMatrix&amp; inputs, RealMatrix <span class="keyword">const</span>&amp; visibleStates)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        m_rbm.<a class="code hl_function" href="classshark_1_1_r_b_m.html#a87ff1500124f108b836beebc4ee0eeb4" title="Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion.">inputHidden</a>(inputs,visibleStates);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    }</div>
</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span> </div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment"></span> </div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">    ///\brief Calculates the input of the visible neurons given the state of the hidden.</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    ///@param inputs the vector the input of the visible neurons is stored in</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    ///@param hiddenStates the state of the hidden neurons</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    ///@todo Remove this and replace fully by the rbm method if possible</span></div>
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#acf73968e06c43cedaf8fede5b2bf7782">  100</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="structshark_1_1_energy.html#acf73968e06c43cedaf8fede5b2bf7782" title="Calculates the input of the visible neurons given the state of the hidden.">inputVisible</a>(RealMatrix&amp; inputs, RealMatrix <span class="keyword">const</span>&amp; hiddenStates)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        m_rbm.<a class="code hl_function" href="classshark_1_1_r_b_m.html#ab6140a0df931943c1c26bc07065a5565" title="Calculates the input of the visible neurons given the state of the hidden.">inputVisible</a>(inputs,hiddenStates);</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }</div>
</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    <span class="comment"></span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    ///\brief Computes the logarithm of the unnormalized probability of each state of the</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// hidden neurons in a batch by using the precomputed input/activation of the visible neurons.</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    ///@param hiddenState the batch of states of the hidden neurons</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment">    ///@param visibleInput the batch of current inputs for he visible units given hiddenState</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">    ///@param beta the inverse temperature</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    ///@return the unnormalized probability</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> BetaVector&gt;</div>
<div class="foldopen" id="foldopen00112" data-start="{" data-end="}">
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#afa5365f469f52d0682d3820fb071fa1b">  112</a></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#afa5365f469f52d0682d3820fb071fa1b" title="Computes the logarithm of the unnormalized probability of each state of the hidden neurons in a batch...">logUnnormalizedProbabilityHidden</a>(</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        RealMatrix <span class="keyword">const</span>&amp; hiddenState, </div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        RealMatrix <span class="keyword">const</span>&amp; visibleInput, </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        BetaVector <span class="keyword">const</span>&amp; beta</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(hiddenState.size1()==visibleInput.size1());</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(hiddenState.size1()==beta.size());</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = hiddenState.size1();</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        </div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="comment">//calculate the energy terms of the hidden neurons for the whole batch</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        RealVector energyTerms = m_hiddenNeurons.energyTerm(hiddenState,beta);</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <span class="comment">//calculate resulting probabilities in sequence</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        RealVector p(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>);</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>; ++i){</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>            p(i) = m_visibleNeurons.logMarginalize(row(visibleInput,i),beta(i))+energyTerms(i);</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        }</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        <span class="keywordflow">return</span> p;</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    }</div>
</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span> </div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment"></span> </div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment">    ///\brief Computes the logarithm of the unnormalized probability of each state of the </span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment">    /// visible neurons in a batch by using the precomputed input/activation of the hidden neurons.</span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment">    ///@param visibleState the batch of states of the hidden neurons</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment">    ///@param hiddenInput the batch of current inputs for he visible units given visibleState</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">    ///@param beta the inverse temperature</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment">    ///@return the unnormalized probability</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> BetaVector&gt;</div>
<div class="foldopen" id="foldopen00141" data-start="{" data-end="}">
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a7d21da0cfe60fabb495a5fa0d75cce51">  141</a></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#a7d21da0cfe60fabb495a5fa0d75cce51" title="Computes the logarithm of the unnormalized probability of each state of the visible neurons in a batc...">logUnnormalizedProbabilityVisible</a>(</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        RealMatrix <span class="keyword">const</span>&amp; visibleState,</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        RealMatrix <span class="keyword">const</span>&amp; hiddenInput, </div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        BetaVector <span class="keyword">const</span>&amp; beta</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(visibleState.size1()==hiddenInput.size1());</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(visibleState.size1()==beta.size());</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = visibleState.size1();</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        <span class="comment">//calculate the energy terms of the visible neurons for the whole batch</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        RealVector energyTerms = m_visibleNeurons.energyTerm(visibleState,beta);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        </div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        RealVector p(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>; ++i){</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            p(i) = m_hiddenNeurons.logMarginalize(row(hiddenInput,i),beta(i))+energyTerms(i);</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        }</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="keywordflow">return</span> p;</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    }</div>
</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span> </div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    <span class="comment"></span></div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment">    ///\brief Computes the logarithm of the unnormalized probability for each state of the visible neurons from a batch.</span></div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span><span class="comment">    ///@param visibleStates the batch of states of the hidden neurons</span></div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="comment">    ///@param beta the inverse temperature</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> BetaVector&gt;</div>
<div class="foldopen" id="foldopen00166" data-start="{" data-end="}">
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a7c5d47f64f1839574b55545cebdbea78">  166</a></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#a7c5d47f64f1839574b55545cebdbea78" title="Computes the logarithm of the unnormalized probability for each state of the visible neurons from a b...">logUnnormalizedProbabilityVisible</a>(RealMatrix <span class="keyword">const</span>&amp; visibleStates, BetaVector <span class="keyword">const</span>&amp; beta)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(visibleStates.size1() == beta.size());</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        </div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        RealMatrix hiddenInputs(beta.size(),m_hiddenNeurons.size());</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <a class="code hl_function" href="structshark_1_1_energy.html#a751c81c6de87a9d563d36db38edccb92" title="Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion.">inputHidden</a>(hiddenInputs,visibleStates);</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="structshark_1_1_energy.html#a7d21da0cfe60fabb495a5fa0d75cce51" title="Computes the logarithm of the unnormalized probability of each state of the visible neurons in a batc...">logUnnormalizedProbabilityVisible</a>(visibleStates, hiddenInputs, beta);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>    }</div>
</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    <span class="comment"></span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment">    ///\brief Computes the logarithm of the unnormalized probability of each state of the hidden neurons from a batch.</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">    ///@param hiddenStates a batch of states of the hidden neurons</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">    ///@param beta the inverse temperature</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> BetaVector&gt;</div>
<div class="foldopen" id="foldopen00179" data-start="{" data-end="}">
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a3e25296fca0f1f23803bbb1535ff8f96">  179</a></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#a3e25296fca0f1f23803bbb1535ff8f96" title="Computes the logarithm of the unnormalized probability of each state of the hidden neurons from a bat...">logUnnormalizedProbabilityHidden</a>(RealMatrix <span class="keyword">const</span>&amp; hiddenStates, BetaVector <span class="keyword">const</span>&amp; beta)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(hiddenStates.size1() == beta.size());</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        </div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        RealMatrix visibleInputs(beta.size(),m_visibleNeurons.size());</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <a class="code hl_function" href="structshark_1_1_energy.html#acf73968e06c43cedaf8fede5b2bf7782" title="Calculates the input of the visible neurons given the state of the hidden.">inputVisible</a>(visibleInputs,hiddenStates);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="structshark_1_1_energy.html#afa5365f469f52d0682d3820fb071fa1b" title="Computes the logarithm of the unnormalized probability of each state of the hidden neurons in a batch...">logUnnormalizedProbabilityHidden</a>(hiddenStates, visibleInputs, beta);</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>    }</div>
</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment"></span> </div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="comment">    ///\brief Optimization of the calculation of the energy, when the input of the hidden units is already available.</span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span><span class="comment">    ///@param hiddenInput the vector of inputs of the hidden neurons</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span><span class="comment">    ///@param hidden the states of the hidden neurons</span></div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment">    ///@param visible the states of the visible neurons</span></div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="comment">    ///@return the value of the energy function</span></div>
<div class="foldopen" id="foldopen00192" data-start="{" data-end="}">
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a25ad87baa9a3500ea7b3af9a4effc933">  192</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#a25ad87baa9a3500ea7b3af9a4effc933" title="Optimization of the calculation of the energy, when the input of the hidden units is already availabl...">energyFromHiddenInput</a>(</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        RealMatrix <span class="keyword">const</span>&amp; hiddenInput,</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        RealMatrix <span class="keyword">const</span>&amp; hidden, </div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        RealMatrix <span class="keyword">const</span>&amp; visible</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>        RealMatrix <span class="keyword">const</span>&amp; phiOfH = m_hiddenNeurons.phi(hidden);</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = hiddenInput.size1();</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        RealVector energies(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>);</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>; ++i){</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>            energies(i) = -inner_prod(row(hiddenInput,i),row(phiOfH,i));</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        }</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        energies -= m_hiddenNeurons.energyTerm(hidden,blas::repeat(1.0,<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>));</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        energies -= m_visibleNeurons.energyTerm(visible,blas::repeat(1.0,<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>));</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        <span class="keywordflow">return</span> energies;</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    }</div>
</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span> </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span><span class="comment"></span> </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span><span class="comment">    ///\brief Optimization of the calculation of the energy, when the input of the visible units is already available.</span></div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment">    ///@param visibleInput the vector of inputs of the visible neurons</span></div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">    ///@param hidden the states of the hidden neurons</span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span><span class="comment">    ///@param visible the states of the visible neurons</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="comment">    ///@return the value of the energy function</span></div>
<div class="foldopen" id="foldopen00214" data-start="{" data-end="}">
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"><a class="line" href="structshark_1_1_energy.html#a9f124cd4e0a4efa781ab34ab246fca81">  214</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="structshark_1_1_energy.html#a9f124cd4e0a4efa781ab34ab246fca81" title="Optimization of the calculation of the energy, when the input of the visible units is already availab...">energyFromVisibleInput</a>(</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        RealMatrix <span class="keyword">const</span>&amp; visibleInput,</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        RealMatrix <span class="keyword">const</span>&amp; hidden, </div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        RealMatrix <span class="keyword">const</span>&amp; visible</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        RealMatrix <span class="keyword">const</span>&amp; phiOfV = m_visibleNeurons.phi(visible);</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = visibleInput.size1();</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        RealVector energies(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>);</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>; ++i){</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>            energies(i) = -inner_prod(row(phiOfV,i),row(visibleInput,i));</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        }</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        energies -= m_hiddenNeurons.energyTerm(hidden,blas::repeat(1.0,<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>));</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        energies -= m_visibleNeurons.energyTerm(visible,blas::repeat(1.0,<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>));</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        <span class="keywordflow">return</span> energies;</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>    }</div>
</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>    <a class="code hl_class" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a> <span class="keyword">const</span>&amp; m_rbm;</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>    <a class="code hl_typedef" href="structshark_1_1_energy.html#aff1c0d419e5e79be2221262c2321a7c8">HiddenType</a> <span class="keyword">const</span>&amp; m_hiddenNeurons;</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>    <a class="code hl_typedef" href="structshark_1_1_energy.html#ab71e2d1ae4d13995eaccf3b4992f3593">VisibleType</a> <span class="keyword">const</span>&amp; m_visibleNeurons;    </div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>};</div>
</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span> </div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>}</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span> </div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="preprocessor">#endif</span></div>
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